createGP {mlegp}R Documentation

creates a Gaussian process object

Description

creates a Gaussian process gp object

Usage

createGP(X, Z, beta, a, meanReg, sig2, nugget, param.names = 1:dim(X)[2], constantMean = 1)

Arguments

X the design matrix
Z output obtained from the design matrix X, as a vector or a 1-column matrix
beta vector of correlation coefficients
a vector of smoothness parameters in the correlation function (if mlegp is used, these will be 2)
meanReg the constant mean if constantMean = 1, otherwise the regression coefficients of the mean function such that meanReg pre-multiplied by (1 X) will produce the mean matrix
sig2 the unconditional variance of the Gaussian process
nugget the constant nugget or a vector of length nrow(X) corresponding to the diagonal nugget matrix
param.names optional vector of parameter names (with length equal to ncol(X)
constantMean 1 if the Gaussian process has a constant mean; 0 otherwise

Value

an object of class gp that contains the following components:

Z matrix of observations
numObs number of observations
X the design matrix
numDim number of dimensions of X
constantMean 1 if GP has a constant mean; 0 otherwise
mu the mean matrix
Bhat mean function regression coefficients
beta correlation parameters
a smoothness parameters in correlation function
sig2 unconditional variance of predicted expected output
params vector of parameter names, corresponding to columns of X
invVarMatrix inverse var-cov matrix
nugget constant nugget or vector corresponding to the diagonal nugget matrix
loglike the log likelihood of the observations
cv results from cross-validation, where cv[,1] are the cross-validated predictions cv[,2] are the variances of the cross-validated predictions

Note

this function is called by mlegp and should not be called by the user

Author(s)

Garrett M. Dancik dancikg@nsula.edu

References

http://users.nsula.edu/dancikg/mlegp/

See Also

mlegp


[Package mlegp version 2.2.6 Index]